26106 results about "Road surface" patented technology

A multi-task joint perception network model and detection method for traffic road surface information can simultaneously detect a lane line and a drivable area. A coordinate attention mechanism is integrated into a traditional feature extraction network to ensure that a feature extraction effect is enhanced while a calculated amount is not increased. In a neck network, a dilated convolution residual module is proposed to enhance performance of prediction of details by the network, and a decoder part shares features of the drivable area into lane line detection to enhance a lane line detection effect under complex road conditions. In a training stage, there is provided a alternating optimization training method to improve integral segmentation performance of the model. The multi-task joint perception network model and detection method realizes quite high accuracy and excellent speed performance in a challenging BDD100K dataset.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

The invention belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

The invention discloses an asphalt pavement health state monitoring system and method, and belongs to the technical field of traffic infrastructure monitoring. The system comprises the following modules: a multi-source data acquisition module used for realizing real-time acquisition of pavement state parameters; the data processing module is used for performing space-time alignment, quality evaluation, exception handling and standardization on the acquired multi-source data; the multi-scale feature extraction module is used for extracting features from the microscopic diseases, the road section states and the network level, and constructing a multi-scale feature representation system; the dynamic graph analysis module is used for constructing a layered dynamic graph structure and realizing unified modeling and analysis of the road network health state; the health assessment module is used for realizing real-time assessment, deterioration trend prediction and risk early warning of the road surface health state; and the decision visualization module is used for displaying the monitoring data, the prediction result and the early warning information, and automatically generating maintenance decision suggestions based on the evaluation result.

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

An embodiment related to a system, wherein the system is operable to determine a road surface condition, wherein the road surface condition is at least one of an ice, wet, and snow; adjust, a safe distance value of the host vehicle based on the road surface condition; detect a vehicle type, a speed, and a visible roof area of a target vehicle; determine an uphill road that the host vehicle is approaching; determine that the target vehicle is slowing down based on a change in speed of the target vehicle in real-time; and determine, a collision avoidance action for the host vehicle to avoid a collision with the target vehicle.

Disclosed in the present invention are a rapid large-range asphalt-pavement quality assessment method and system based on high-resolution remote sensing. The method comprises the following steps: acquiring a high-resolution multispectral image and panchromatic image of a pavement area of a road network area to be subjected to assessment; generating superpixels of the pavement area on the basis of the high-resolution multispectral image and panchromatic image of the pavement area; removing superpixels of a non-asphalt pavement from the superpixels of the pavement area, so as to obtain superpixels of an asphalt pavement in the pavement area; on the basis of the superpixels of the asphalt pavement, performing calculation to obtain a brightness indicator, texture indicator, structure indicator and spectrum indicator of each superpixel of the asphalt pavement; and according to the calculated brightness indicator, texture indicator, structure indicator and spectrum indicator, performing calculation to obtain a remote-sensing quality assessment index corresponding to each superpixel of the asphalt pavement, and then obtaining a quality situation assessment diagram for the asphalt pavement. By means of the present invention, various assessment indexes are comprehensively used, thereby improving the precision and visibility of an assessment result, and reducing the manual intervention.

The invention relates to the technical field of vehicle control, and provides a new energy vehicle operation optimization method and system based on intelligent networking, and the method comprises the steps: collecting a first operation parameter of a vehicle at a current moment and a road parameter at a next moment through a vehicle-mounted sensor network; wherein the first operation parameter comprises a tire movement value corresponding to vehicle suspension deformation variable data based on a current pavement coefficient, and the road parameter is road feature data of a road to be driven in; according to the first operation parameters and the road feature data, a load mean value model and an acceleration safety threshold value of the current vehicle in the road to be driven in are constructed; according to the load mean value model, a damping adjusting instruction of the vehicle suspension is generated; and generating a driving torque vector distribution scheme according to the acceleration safety threshold.

The present invention relates to the technical field of highway maintenance management, and in particular to a highway maintenance safety management system. The system includes a road condition monitoring module, a traffic load assessment module, a traffic flow redistribution module, a maintenance timing determination module, and a maintenance effect verification module, which are used to monitor multi-layer road condition data of the highway, and perform table-deep correlation detection to obtain road surface abnormal point data; quantify the road surface loss risk of the road surface abnormal point data to obtain road surface loss early warning indicators; collect vehicle images and meteorological data; analyze vehicle model loss contribution based on vehicle images; construct an environmental impact coefficient matrix based on meteorological data; construct a dynamic weight model based on vehicle model loss contribution and environmental impact coefficient matrix to obtain a road surface loss dynamic weight model. The present invention improves the efficiency, benefit and intelligence level of highway maintenance management by organically integrating highway maintenance with traffic management.

The invention relates to a pavement structure damage detection method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image detection data, radar detection data and sound wave detection data of a to-be-detected road surface; performing structural damage dual-channel identification on the image detection data, the radar detection data and the sound wave detection data to obtain initial structural damage detection data of the to-be-detected road surface; according to the preliminary structural damage detection data, determining physical model use guide information and physical model setting parameters corresponding to the to-be-detected road surface; and according to the physical model use guide information and the physical model setting parameters, performing mechanical analysis and fluctuation propagation analysis on the to-be-detected pavement by using each physical analysis model to obtain pavement structure damage detection data of the to-be-detected pavement. By adopting the method, the accuracy and reliability in pavement structure damage detection can be effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to a road surface automatic inspection robot path planning method and device based on artificial intelligence, and the method comprises the steps: collecting road surface environment data in real time through various sensors, carrying out the feature extraction through a convolutional neural network based on the road surface environment data, and constructing a dynamic environment model through a graph convolutional network. According to the method, the change trend of potential road surface damage areas and obstacles is predicted, path planning and optimization are performed by using a deep reinforcement learning algorithm, after path planning is completed, the robot autonomously travels according to a planned path, and real-time adaptive adjustment is performed on the path through an online learning method so as to cope with environmental changes. Collected data and path planning information are fed back to the central control unit in real time to be stored, and a road surface health condition report is generated. According to the invention, the efficiency and the precision of the inspection task are improved, and the method has high environmental adaptability and is suitable for complex and dynamically changing road environments.

The invention discloses a braking pressure degradation strategy of an electronic mechanical braking system, and belongs to the technical field of vehicle braking control, and the braking pressure degradation strategy of the electronic mechanical braking system comprises the following steps: S1, multi-sensor fusion is carried out to correct a friction coefficient in real time, and a high-precision gridding pavement model is constructed; s2, performing time sequence prediction on braking torque distribution, dynamically constraining a braking pressure threshold value and performing pre-adjustment; s3, reinforcement learning is carried out to generate a prospective degradation sequence, and thermal coupling and pressure fluctuation control are optimized; s4, dynamically encrypting the hierarchical communication architecture, and ensuring that the instruction is complete and consistent with the time sequence through redundancy switching; s5, generating an adversarial network compensation failure signal, and dynamically adjusting the weight of the sensor to resist interference; s6, triggering a man-machine interaction strategy in a grading manner, and dynamically adjusting a safety threshold in combination with a driving behavior; s7, carrying out simulation verification on the digital twin platform, and carrying out fault injection encryption to update full-life-cycle parameters; the method has the beneficial effects that self-adaptive optimization and hierarchical fault tolerance of the brake pressure are realized, and the system safety and response efficiency are improved.

The invention discloses an asphalt concrete pavement early crack detection method and system, and the method comprises the steps: S1, obtaining pavement three-dimensional point cloud data to form a texture image, recognizing a gray level change region, and marking the region as a suspected crack region as a first crack degree; s2, segmenting a suspected crack region through a deep learning model Mask R-CNN, determining a crack type, and measuring geometric parameters of the crack as a second crack degree; s3, in combination with historical data and environmental factors, the development trend and the damage degree of the crack are evaluated through a machine learning model to serve as a third crack degree; s4, establishing a crack detection mechanism, and recording and tracking the state change of the early asphalt concrete pavement crack; and S5, setting and comparing crack state evaluation thresholds, and performing early warning when the crack state evaluation thresholds exceed a threshold boundary mechanism. According to the invention, an early crack state tracking and early warning mechanism is realized, and the efficiency and quality of pavement maintenance work are improved.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

The invention relates to the field of road construction, and discloses a machine vision-based fabricated pavement precast slab flatness measurement method, which comprises the following steps of: positioning and marking an assembled fabricated precast slab area; generating point cloud data through three-dimensional scanning, and carrying out denoising processing and feature extraction; calculating the elevation of each point on the surface of the precast slab according to the feature data, identifying elevation change and generating flatness evaluation data; performing gray level detection on the three-dimensional point cloud data, and further analyzing a defect area in combination with elevation data; and utilizing the Gaussian process regression model to predict the elevation of the unscanned area or the boundary area, and finally outputting a flatness evaluation result. Through the scheme of the invention, efficient and accurate detection of the surface flatness of the prefabricated slab can be realized, feedback and adjustment suggestions are provided for construction, the detection precision is effectively improved, and the construction cost is reduced.

The invention discloses a traceable road surface runoff pollutant monitoring method and system, and relates to the technical field of water pollution monitoring. The method comprises the following steps: capturing environmental factors of precipitation, air temperature and traffic flow according to road surface basic monitoring data, extracting pollutant concentration data of sampling points, analyzing relevance of the environmental factors, identifying and classifying pollutant concentrations, and obtaining pollutant concentration change trend characteristics. According to the method, the relationship between the environmental factors and the pollutants is accurately analyzed by capturing the environmental factors and the pollutant concentration data, the understanding of the change trend of the pollutant concentration is improved, the pre-judgment precision of future changes is effectively improved, in real-time data comparison, error analysis helps to identify deviations and adjust model weights, the prediction accuracy is improved, and the prediction accuracy is improved. Through monitoring resource demand prediction, optimization of monitoring point layout and resource configuration and dynamic adjustment of a monitoring strategy, pollutant concentration monitoring is more flexible and accurate, and monitoring efficiency and response speed are improved.

The invention discloses a road disease detection system and method based on multi-source data fusion. The system comprises a data acquisition module, a data preprocessing module, a disease detection module, a three-dimensional modeling module, a data analysis module and a maintenance module. The data acquisition module is used for acquiring road surface data and sending the acquired data to the data preprocessing module; the road surface data comprises road data, road disease data, road vehicle data, non-motor vehicle data and pedestrian data, compared with the prior art, more comprehensive and more accurate detection of road diseases is realized by fusing multi-source data; by means of multi-dimensional data acquisition of radar data, video data, three-dimensional point cloud data, geographical location information and the like, the limitation of a single data source is overcome; the data preprocessing module can effectively remove noise and abnormal values, accurately classify and mark diseases, and fuse data collected by different sensors, so that the quality and integrity of the data are improved.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention discloses an intelligent pavement disease diagnosis method based on image recognition, and relates to the technical field of pavement disease diagnosis, and the method comprises the steps: deploying an image collection device and a pavement monitoring sensor in a target pavement region, so as to obtain multi-source pavement data; preprocessing the multi-source road surface data, and performing feature extraction to obtain a road surface feature sequence; and based on a deep learning algorithm and in combination with the pavement feature sequence, learning features of different disease types, constructing a disease identification classification model, and identifying different types of pavement diseases. Through the high-definition camera and the image processing technology, various disease types such as cracks, pit slots and ruts can be quickly and accurately identified, and in combination with a deep learning algorithm, disease features can be automatically extracted, high-precision identification of road diseases is realized, the disease detection efficiency is remarkably improved, manual intervention is reduced, and the detection efficiency is improved. And timely and accurate data support is provided for road maintenance.

The invention relates to the technical field of road maintenance and management, in particular to a pavement internal disease analysis method and system based on artificial intelligence. Five core processes of multi-layer sensor network real-time data acquisition, multi-modal data fusion and deep learning feature extraction, dynamic environment and use mode combined disease prediction and analysis, intelligent prediction and decision support system, and real-time disease condition evaluation and report generation are carried out. Through the technical means, comprehensiveness and timeliness of data acquisition, accuracy of disease identification and prediction, scientificity of a maintenance strategy and optimization of resource allocation are realized; the finally generated disease current situation report provides a detailed disease distribution diagram, disease type analysis and disease severity evaluation, comprehensive, accurate and timely disease information is provided for maintenance personnel, and the efficiency and effect of pavement maintenance and management are remarkably improved.

The invention discloses a municipal road pavement crack multi-modal fusion detection method, which belongs to the technical field of pavement crack detection, and comprises the following steps: S1, obtaining visible light images, infrared thermal imaging and three-dimensional laser scanning data in multi-modal data, generating a time-unified and space-aligned multi-modal data set based on the three-dimensional laser scanning data; s2, extracting a visible light image and an infrared thermal image from the multi-modal data set, and obtaining features corresponding to the visible light image and the infrared thermal image to obtain a multi-modal feature set; and S3, inputting the multi-modal feature set into a deep neural network, and carrying out weighted integration on feature vectors through multi-layer convolution operation to obtain a crack detection result with three-dimensional coordinates. The municipal road pavement crack multi-modal fusion detection method solves the problem that an existing pavement crack detection method is low in accuracy and reliability.

The invention relates to the technical field of environment monitoring and early warning, in particular to a road and bridge pavement environment monitoring and early warning method and system. The method comprises the following steps: arranging acquisition equipment for acquiring pavement environment data, wherein the pavement environment data comprises pavement ice and snow state data; the collected road surface environment data are preprocessed; training an environment monitoring and early warning model based on the road historical environment data to obtain a trained environment monitoring and early warning model, and inputting the preprocessed environment data into the environment monitoring and early warning model for monitoring and early warning; comparing a monitoring and early warning analysis result with a preset threshold value, and determining whether early warning needs to be sent out according to a comparison result; meanwhile, when ice and accumulated snow are detected to appear on the road surface, the position and the size of the ice and the accumulated snow are marked in visual equipment. The system comprises a data acquisition module, a model training module and an early warning judgment module. According to the invention, a comprehensive and efficient road and bridge pavement environment monitoring and early warning system is formed.

The invention provides an intelligent asphalt pavement segregation detection method based on multi-source data analysis, and relates to the technical field of road engineering detection.The method comprises the steps that a to-be-detected road section is determined, and multi-source data of the to-be-detected road section is obtained; preprocessing the multi-source data; constructing a double-branch neural network detection model which comprises a first branch detection model and a second branch detection model, and inputting the preprocessed optical data and laser point cloud data into the pre-trained first branch detection model to obtain a grading segregation distribution result; inputting the preprocessed thermal imaging data into a pre-trained second branch detection model to obtain a temperature segregation distribution result; performing conjoint analysis on the grading segregation distribution result and the temperature segregation distribution result to generate a comprehensive segregation index; and outputting a detection result according to the comprehensive segregation index. According to the invention, comprehensive, accurate and automatic detection of the segregation degree of the asphalt pavement can be realized, and a scientific basis is provided for pavement quality evaluation and maintenance.

The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior.

The invention relates to a vehicle control method, device and equipment, a readable storage medium and a program product. The method comprises the steps that when a target vehicle is in an auxiliary driving state, a road surface image and vehicle dynamics information of the target vehicle are collected; determining a first road adhesion coefficient based on the road image; determining a second road adhesion coefficient based on the vehicle dynamics information; fusing the first pavement adhesion coefficient and the second pavement adhesion coefficient to obtain a fused pavement adhesion coefficient; and determining vehicle control parameters based on the fused road adhesion coefficient, and performing driving control on the target vehicle based on the vehicle control parameters. By adopting the method, the control accuracy of the vehicle can be improved.

The invention relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method, and the system comprises an image acquisition module, a deep learning module, an image enhancement module, a crack measurement and calculation module, a crack development trend prediction module and an information visualization module. The core innovation of the invention lies in that a differential geometry theory is introduced to construct a crack development trend prediction module, and the module comprises a crack characterization model based on a differential manifold, a multi-scale crack evolution tensor field analysis model and a nonlinear space-time crack development prediction model. A pavement is regarded as a two-dimensional differential manifold, cracks are represented as singular curves on the manifold, a multi-scale tensor field analysis technology and a non-linear kinetic equation are combined, accurate prediction of the future development trend of the cracks is achieved, the system supports multiple image acquisition modes, transverse, longitudinal and net cracks can be accurately detected, and the detection precision is high. The method adapts to complex illumination and background conditions, and predicts the expansion rate and severity change of the crack.

The invention relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

The invention discloses an intelligent automobile electro-hydraulic composite braking system integrating driver intention, and belongs to the field of intelligent automobile braking control. The system comprises a vehicle environment fusion sensing module, a braking intention identification module, a multi-target braking force distribution module, a regenerative braking torque constraint module and an optimal distribution coefficient decision module. When a driver brakes, the vehicle environment fusion sensing module is used for acquiring vehicle driving state information, road adhesion coefficient information, driving environment information and driver state information; the braking intention identification module adopts a neural network algorithm to accurately identify the braking intention of the driver; the multi-target braking force distribution module optimizes the required braking torque by using an NSGA-II multi-target optimization algorithm; the regenerative braking torque constraint module dynamically corrects the calculation result of the regenerative braking torque through a comprehensive constraint condition; and the optimal distribution coefficient decision module screens out the optimal braking distribution coefficient under the current working condition from the Pareto solution set through dynamic weighting.

The invention provides a pavement crack detection method and apparatus, and an electronic device. The method comprises the steps of obtaining a crack image and a crack prediction model obtained by pre-training; wherein the crack prediction model comprises a visual modeling model, an inference model and a dynamic multi-modal fusion network; performing hidden space global visual modeling on the crack image based on a visual modeling model to obtain hidden space global visual features; generating a semantic mask matrix guided by global scene semantics for the crack image based on an inference model; and inputting the hidden space global visual features and the semantic mask matrix into a dynamic multi-modal fusion network, and predicting to obtain crack information. According to the invention, pixel-level prediction of the crack can be carried out, accurate detection of the pavement crack is realized, and technical support is provided for pavement maintenance.